The present invention relates to digital signal processing, and more particularly to architectures and methods for enhancement in digital images and video.
Imaging and video capabilities have become the trend in consumer electronics. Digital cameras, digital camcorders, and video cellular phones are common, and many other new gadgets are evolving in the marketplace. Advances in large resolution CCD/CMOS sensors coupled with the availability of low-power digital signal processors (DSPs) has led to the development of digital cameras with both high resolution still image and short audio/visual clip capabilities. The high resolution (e.g., sensor with a 2560×1920 pixel array) provides quality offered by traditional film cameras.
FIG. 2a is an example functional block diagram for digital camera control and image processing (“image pipeline”). The automatic focus, automatic exposure, and automatic white balancing are referred to as the 3A functions; and the image processing includes functions such as color filter array (CFA) interpolation, gamma correction, white balancing, color space conversion, and JPEG/MPEG compression/decompression (JPEG for single images and MPEG for video clips). Note that the typical color CCD consists of a rectangular array of photosites (corresponding to pixels in an output image) with each photosite covered by a filter (the CFA): typically, red, green, or blue. In the commonly-used Bayer pattern CFA one-half of the photosites are green, one-quarter are red, and one-quarter are blue. FIGS. 2b-2d show an alternative architecture with FIG. 2c illustrating the video frontend of the FIG. 2b processor, and FIG. 2d the preview engine (PRV) of the processor.
Typical digital cameras provide a capture mode with full resolution image or audio/visual clip processing plus compression and storage, a preview mode with lower resolution processing for immediate display, and a playback mode for displaying stored images or audio/visual clips.
High contrast images are appealing to human eyes. However, it is difficult to obtain high contrast images from video or still cameras or camera phones, due to the limitations of the sensors, image processors, and displays. Many contrast enhancement methods have been proposed for image processing applications. But they are either too complex to be used for consumer video or still cameras, or specific for different imaging applications such as biomedical imaging. A desirable method for digital cameras should be universal, because digital cameras will be used to capture different kinds of images. It also should have low computation complexity and low memory requirements, due to the cost and shot-to-shot constraints of digital cameras.
Starck et al., “Gray and Color Image Contrast Enhancement by the Curvelet Transform”, 12 IEEE Trans. Image Processing, 706 (June 2003) discloses a complex transform on images, resulting in high computational complexity and high memory requirements.